Systemic Risk of Dual Banking Systems...Abedifar et al. (RF, 2013), Bourkhis and Nabi (RFE, 2013). Beck et al. (JBF, 2013), Cihak and Hesse (JFSR, 2010), Imam and Kpodar (EM, 2013)

Introduction Methodology Application and Results

Systemic Risk of Dual Banking Systems

S. Q. Hashem1 P. Giudici2 P. Abedifar3

1&2Faculty of EconomicsUniversity of Pavia

3Faculty of ManagementUniversity of St Andrews

September 2016


Summary

Context

Dual Banking systems - countries where different bank types operate:

ConventionalIslamicHybrids - conventional banks with islamic windows

Aim

Measuring systemic risk contributions of different bank types in dualbanking systems

Methods

Systemic risk measures, powered with correlation network models, appliedto GCC countries


Background

The 2007-2008 financial crisis had a negative effect on both Islamicbanks (IB) and conventional banks (CB).Some differences were found between the IB and CB risk andperformance level in terms of the crisis impact, eg a time laggedprofitability deterioration effect for IB.No direct comparison available between IB and CB in terms ofsystemic risk. No analysis of hybrid type effects


Research Aim

To investigate the systemic importance of CB, IB and hybrid bank types,at the aggregate level.

The systemic risk contribution of each banking sector (country X type)is determined using market based systemic measures: MES, SRISKand ∆CoVaR, powered with correlation networks

Data on banking systems of GCC countries, which:Are key players with nearly 38% of IB assets (IFSB, 2014)Have economies that are oil dependent.Have relatively homogeneous economic and financial development.


Literature and Contribution

Systemic risk measures based on market prices:

Adrian and Brunnermeier (2010), Acharya et al. (2010), Brownleesand Engle (2012)

Correlation networks in finance:

Ahelegby, Billio (JAE 2015), Giudici and Spelta (JBES 2015), Giudici,Sarlin and Spelta (This conference: capital flows), Giudici and Parisi(This conference: sovereign risk)

Financial risks in dual banking systems:

Abedifar et al. (RF, 2013), Bourkhis and Nabi (RFE, 2013). Beck et al.(JBF, 2013), Cihak and Hesse (JFSR, 2010), Imam and Kpodar (EM,2013)

Our contribution:

A novel partial correlation based measure (NetMES).Systemic risk contributions in dual banking systems.


The Systemic Risk Measures

MES (Acharya et al., 2010)Acharya et al. (2010) Marginal Expected Shortfall: bank market loss expectedif market returns are less than a given threshold C (C =-2%).

MESit (C) = σit ρit Et−1(εmt |εmt <Cσmt

) + σit

√1− ρ2

it Et−1(ξit |εmt <Cσmt

)

MES is expressed as a weighted function of the tail expectation of thestandardized market residual and the tail expectation of the standardizedidiosyncratic firm residual


Systemic Risk Measures

SRISK (Brownlees and Engle, 2012).SRISK: expected capital shortfall of a given financial institution, conditional ona crisis affecting the whole financial system.The average of the future expected loss of the system due to a crisis over thenext six months is approximated using daily MES asLRMES ' 1− exp(−180×MESit )

SRISKit =((k(Debtit + Equityit )− Equityit )|Cit

)= max

([kLit − 1 + (1− k)LRMESit

]Wit)

Wit is the market value of the institution, (quasi) leverage is defined asLit = (Dit + Wit )/Wit , and k = 8% is the minimum fraction of the capital ratiothat each bank needs to hold.


Systemic Risk Measures

∆CoVaR (Adrian and Brunnermeier, 2011)CoVaR is the VaR of the market portfolio return m conditional on a tail eventC(rst ) observed for banking sector s as it becomes under financial distress.The ∆CoVaR of a sector s reflects its contribution to systemic risk byassessing the difference between the VaR of the financial system conditionalon banking sector s being under financial distress and the VaR of the systemconditional on banking sector s being in its median state.

∆CoVaRst (α) = CoVaRm|rst =VaRst (α)t − CoVaRm|rst =Median(rst )

t


Dynamic conditional correlations

Dynamic Conditional Correlation model: DCCFollowing Brownlees and Engle (2012) we use a bivariate GARCH model forthe demeaned returns process:

rt = H1/2t εt

rt = (rmt rst )′ represents the vector of market and banking sector returns.

εt = (εmt ξst )′ represents a vector of i.i.d . standardized innovations,

assumed to be unknown with no assumptions regarding the bivariatedistributions.εt has a mean E(εt ) = 0 and an identity covariance matrix ofE(εt ε

′t ) = I2.

The time varying conditional variance-covariance matrix Ht is defined as:

Ht =

(σ2

mt σmt σit ρitσmt σit ρit σ2

it

)where σmt and σit represent the conditional standard deviation for the systemand the firm respectively, and ρit represents the time varying conditionalcorrelation.


Partial correlations

We improve the DCC implementation of systemic risk measures replacingcorrelations with partial correlations, obtained from correlation networks.

Operationally, the partial correlation coefficient ρijV is obtained from thecorrelation of the residuals from the regression of Xi on all other variables(excluding Xj ) with the residuals from the regression of Xj on all othervariables (excluding Xi )as in the following:

ρijV = ( εXi |XV\{j} , εXj |XV\{i})

ρijV measures the additional contribution of variable Xj to the variability of Xi

not already explained by the others, and vice versa.


Data description

16 banking sectors constructed from 79 publicly traded deposit-takinginstitution, in 6 GCC countries: Bahrain (BH), Kuwait (KW), Qatar(QA), United Arab Emirates (AE), Saudi Arabia (SA) and Oman (OM).daily stock market returns for financial institutions and country specificmarket indexes.Extends over 10 years, with three periods: Pre-crisis (Jan 2005−Dec2006), Crisis (Jan 2007−Dec 2008), Post-Crisis (Jan 2009−Dec 2014)Aggregated per banking sector type at country level using marketcapitalization weights to construct the sectorial returns rst =

∑nsi=1 wit rit

where wit = mvit/∑ns

i=1 mvit represents the weight of the i-th bank inthe specified banking sector s at time t , given by its marketcapitalization mvit relative to the sector aggregate capitalization∑ns

i=1 mvit .


Methods

We consider the following implementations:First, we use the standard method with the stock market return index for eachdual banking system (country)Second, we replace partial correlations with correlations. The advantage ofdoing so is to use the true direct correlation between two sectors, rather thanon correlation that contains also indirect (spurios) effects.Third, we repeat the first standard method but with the crude oil return index,instead of the market index.


Methods

We use two aggregation levels for banking sectors’ MES:

Aggregate country levelAggregate GCC level

For the aggregation we follow the concept of the CES measure provided byBanulescu and Dumitrescu (2015), and refer to this as the global marginal expectedshortfall GMESjt , as follows:

GMESjt =

ns∑s=1

wst MESst ,

in which j denotes the country, wst = mvst/∑nj

s=1 mvst represents the weight of thebanking sector s at time t , given by its market capitalization mvst relative to theaggregate banking capitalization of that sector

∑nsi=1 mvjt .

The same construction is repeated at the GCC overall level.


Figure: Countries Comparison of MES

0.000000

0.005000

0.010000

0.015000

0.020000

0.025000

0.030000

0.035000

0.040000

AE_CB AE_CBw AE_IB BH_CB BH_CBw BH_IB KW_CB KW_CBw KW_IB OM_CB OM_CBw OM_IB QA_CBw QA_IB SA_CBw SA_IB

MES-Return IndexMES-Return Index pre-crisis MES-Return Index crisis MES-Return Index post-crisis

-0.002

0

0.002

0.004

0.006

0.008


MES-Partial CorrelationMES-Partial Correlation pre-crisis MES-Partial Correlation crisis MES-Partial Correlation post-crisis

-0.0007

0.0003

0.0013

0.0023

0.0033

0.0043

0.0053

0.0063

0.0073


MES-Oil IndexMES-Oil Index pre-crisis MES-Oil Index crisis MES-Oil Index post-crisis


Figure: Countries Comparison of SRISK

-5000000

5000000

15000000

25000000

35000000

45000000

55000000


SRISK-Return IndexSRISK-Return Index pre-crisis SRISK-Return Index crisis SRISK-Return Index post-crisis

0

10000000

20000000

30000000

40000000

50000000

60000000

70000000

80000000


SRISK-Partial CorrelationSRISK-Partial Correlation pre-crisis SRISK-Partial Correlation crisis SRISK-Partial Correlation post-crisis

-5000000

5000000

15000000

25000000

35000000

45000000

55000000

65000000

75000000


SRISK-Oil Index

SRISK-Oil Index pre-crisis SRISK-Oil Index crisis SRISK-Oil Index post-crisis


Figure: Countries Comparison of ∆CoVaR

0.000000

0.005000

0.010000

0.015000

0.020000

0.025000


Delta CoVaR - Return IndexDeltaCoVar-Return Index pre-crisis DeltaCoVar-Return Index crisis DeltaCoVar-Return Index post-crisis

-0.0012

-0.0002

0.0008

0.0018

0.0028

0.0038

0.0048

0.0058

0.0068


DeltaCoVaR-Partial CorrelationDeltaCoVaR-Partial Correlation pre-crisis DeltaCoVaR-Partial Correlation crisis DeltaCoVaR-Partial Correlation post-crisis

-0.001

0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008


DeltaCoVaR-Oil Index

DeltaCoVaR-Oil Index pre-crisis DeltaCoVaR-Oil Index crisis DeltaCoVaR-Oil Index post-crisis


Figure: Global Gulf Risk Measure

0

0.005

0.01

0.015

0.02

0.025

CB CBw IB

MES-Return Index

pre-crisis crisis post-crisis

0

5000000

10000000

15000000

20000000

25000000

CB CBw IB

SRISK-Return Index


0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

CB CBw IB

∆CoVaR-Return Index


-0.0005

0

0.0005

0.001

0.0015

0.002

0.0025

0.003

CB CBw IB

MES-Partial Correlation


0

5000000

10000000

15000000

20000000

25000000

30000000

CB CBw IB

SRISK-Partial Correlation


0

0.0002

0.0004

0.0006

0.0008

0.001

0.0012

0.0014

0.0016

0.0018

CB CBw IB

∆CoVaR-Partial Correlation


0

0.0005

0.001

0.0015

0.002

0.0025

0.003

0.0035

0.004

CB CBw IB

MES-Oil Index


0

5000000

10000000

15000000

20000000

25000000

CB CBw IB

SRISK-Oil Index


0

0.0005

0.001

0.0015

0.002

0.0025

0.003

0.0035

0.004

CB CBw IB

∆CoVaR-Oil Index



Figure: Global Gulf risk meaasure

-0.022000

-0.002000

0.018000

0.038000

0.058000

0.078000

Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13 Jan-14

GMES-Sector-Return Index

GMES_CB GMES_CBw GMES_IB GMES_Gulf

0.23: Mar-05, IB

-0.012

-0.007

-0.002

0.003

0.008

0.013

0.018


GMES_Sector with Partial Correlation


-0.02: Apr-08, CB

0.02: Sep-08, CB

-0.03: Mar-06, CB

-0.002

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016


GMES_Sector with Oil Index


0.03: Jan-05, IB

0.04: Mar-05, IB

Documents

Systemic Risk of Dual Banking Systems...Abedifar et al. (RF, 2013), Bourkhis and Nabi (RFE, 2013). Beck et al. (JBF, 2013), Cihak and Hesse (JFSR, 2010), Imam and Kpodar (EM, 2013)